Three Technology Trends for Financial Services in 2022

The survey reveals that banks and finance companies will spend $623 billion on IT products and services in 2022. However, 60% of large organizations will use one or more privacy-enhanced computing technologies by 2025.

Generative Artificial Intelligence (AI), Autonomous Systems, and Privacy Enhanced Computing (PEC) Privacy Enhanced Account) are the three technology trends that will gain momentum in financial services in 2022, according to Gartner research. According to a new survey on the topic, these trends are expected to continue to grow over the next two to three years, contributing to the advancement and transformation of financial services institutions.

“While growth is a top priority, the need to manage risk, optimize costs and increase efficiency also requires new technological innovations,” says Moutusi Sau, analyst and vice president of research at Gartner.

“Generative AI enables bank IT executives (CIOs) to deliver technology solutions to businesses in pursuit of revenue growth, while privacy-enhanced computing and autonomous systems are long-term solutions that can provide new options to transform the business of financial services organizations,” he says. SAO.

Gartner analysts expect IT spending by financial services companies to grow 6.1% in 2022 to reach $623 billion worldwide. The largest spending category is IT services, which includes consulting and managed services and accounts for 42% of the industry’s total IT spending, with investments of approximately $264 billion. The fastest growing category is Software, with an expected increase of 11.5%, converting $149 billion.

Collectively, the three emerging technologies identified by Gartner contribute to achieving, growing, and transforming business management goals, and have demonstrated use cases in the financial industry.

Trend 1: Generative AI – Gartner predicts that 20% of all test data for consumer-facing use cases will be generated industrially by 2025. Generative AI learns the digital representation of artifacts from data and creates new, innovative creations that resemble the original, but don’t duplicate it.

In financial services, the application of generative networks and natural language generation can be found in most scenarios for fraud detection, trading forecasting, synthetic data generation, and risk factor modeling. It has potential because of the ability to take personalization to new heights.

Trend 2: Autonomous Systems Autonomous systems are physical systems or self-managed software that learn from their environments and dynamically modify their algorithms in real time to improve their behavior in complex ecosystems. They create an agile set of technical capabilities that support new requirements and situations, improve performance, and defend against potential attacks without human intervention.

Currently, standalone systems are mostly based on software solutions specific to the banking context. However, humanoid robots are appearing in smart branches, which are examples of hardware-based autonomous systems that serve customers and employees. It can be applied in independent debt management, personal finance assistants and automated loans. “Roboadvisors” are essentially low-level stand-alone systems, although there are still trust concerns due to the high level of automation.

Gartner predicts that by 2024, 20% of organizations that sell stand-alone systems or devices will require customers to waive compensation provisions related to behavior learned from their products.

Trend 3: Computing that improves privacy – Privacy Enhancement Computing (PEC) protects the processing of personal data in untrusted environments – critical due to the evolution of privacy and data protection laws, as well as growing consumer concerns. This concept uses a series of privacy protection techniques to allow data mining while respecting compliance requirements.

In financial services, data plays an inherent role in any data analysis, computation, and monetization efforts. PEC is increasingly being adopted in use cases such as fraud analysis, intelligence operations, data sharing, and anti-money laundering.

Gartner predicts that 60% of large organizations will use one or more privacy-enhanced computing technologies in analytics, business intelligence, or cloud computing by 2025.

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